Potential contrast - A new image quality measure

Loading...
Thumbnail Image

Date

2017-01-01

Journal Title

Journal ISSN

Volume Title

Repository Usage Stats

57
views
38
downloads

Citation Stats

Abstract

This paper suggests a new quality measure of an image, pertaining to its contrast. Several contrast measures exist in the current research. However, due to the abundance of Image Processing software solutions, the perceived (or measured) image contrast can be misleading, as the contrast may be significantly enhanced by applying grayscale transformations. Therefore, the real challenge, which was not dealt with in the previous literature, is measuring the contrast of an image taking into account all possible grayscale transformations, leading to the best "potential" contrast. Hence, we suggest an alternative "Potential Contrast" measure, based on sampled populations of foreground and background pixels (e.g. scribbles or saliency-based criteria). An exact and efficient implementation of this measure is found analytically. The new methodology is tested and is shown to be invariant to invertible grayscale transformations.

Department

Description

Provenance

Subjects

Citation

Published Version (Please cite this version)

10.2352/ISSN.2470-1173.2017.12.IQSP-226

Scholars@Duke

Faigenbaum-Golovin

Shira Faigenbaum-Golovin

Phillip Griffiths Assistant Research Professor

I am a Phillip Griffiths Assistant Research Professor at Duke University's math department as well as at the Rhodes Interdisciplinary Initiative, working with Prof. Ingrid Daubechies. In 2021 I completed my Ph.D. at the Department of Applied Mathematics, School of Mathematical Sciences, Tel Aviv University, under the supervision of Prof. David Levin and Prof. Yoel Shkolnisky.

My research interests span several areas, including numerical analysis, mathematical modeling, robust and statistically significant analysis of high-dimensional data. I strive to explore new challenges that arise from high-dimensional data as well as study the story that the data geometry tells by modeling the data and posing new mathematical tools. In particular, my research is in approximation theory in low and high-dimensions, geometric methods for manifold reconstruction, studying the geometry of the base manifold and its fibers, computer vision, image processing.
Notable applications of my current and past research include archaeology, evolutionary anthropology, Bible studies, art investigation, and general history. By applying my research to these diverse areas, I aim to contribute valuable insights and shed light on long debated questions.

My publication list (and most online available papers) can be viewed on Google Scholar.

I am co-organizing the AMS Special Session on Computational techniques to study the geometry of the shape space at Joint Mathematics Meetings (JMM) in San Francisco, CA on Jan 3-6 2024. Registration is open!


Material is made available in this collection at the direction of authors according to their understanding of their rights in that material. You may download and use these materials in any manner not prohibited by copyright or other applicable law.